Control the high-order harmonics cutoff through the combination of chirped laser and static electric field

The high harmonic generation from atoms in the combination of chirped laser pulse and static field is theoretically investigated. For the first time, we explore a further physical mechanism of the significant extension of high harmonic generation cutoff based on three-step model. It is shown that the cutoff is substantially extended due to the asymmetry of the combined field. If appropriate parameters are chosen, the cutoff of high harmonic generation can reach Ip+42Up. Furthermore, an ultrabroad super-continuum spectrum can be generated. When the phases are properly compensated for, an isolated 9 attosecond pulse can be obtained.Comment: 7 pages 5figure

Reversible self-Kerr-nonlinearity in an N-type atomic system through a switching field

We investigate the self-Kerr nonlinearity of a four-level N-type atomic system in 87Rb and observe its reversible property with the unidirectional increase of the switching field. For the laser arrangement that the probe field interacts with the middle two states, the slope and the sign of the self-Kerr nonlinearity around the atomic resonance can not only be changed from negative to positive, but also can be changed to negative again with the unidirectional increasing of the switching field. Numerical simulation agrees very well with the experimental results and dressed state analysis is presented to explain the experimental results

Wavelets, Sobolev Multipliers, and Application to Schrödinger Type Operators with Nonsmooth Potentials

We employ Meyer wavelets to characterize multiplier space Xr,pt(ℝn) without using capacity. Further, we introduce logarithmic Morrey spaces Mr,pt,τ(ℝn) to establish the inclusion relation between Morrey spaces and multiplier spaces. By fractal skills, we construct a counterexample to show that the scope of the index τ of Mr,pt,τ(ℝn) is sharp. As an application, we consider a Schrödinger type operator with potentials in Mr,pt,τ(ℝn)

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.Comment: 14 page

Can digital financial inclusion help reduce migrant workers’ overwork? Evidence from China

IntroductionMigrant workers in China are migrants from the rural to the urban areas who usually work in the cities and return to the countryside after a certain period. Due to China’s strict household registration system, they differ significantly from urban residents’ access to public services. However, at the same time, China’s workers are facing a severe phenomenon of overwork, and the group of migrant workers is even more hard-hit by overwork, which will cause various adverse effects on workers and society and should attract the attention of all sectors of society.MethodsThis paper focuses on the impact of digital financial inclusion on the overwork of migrant workers. This study considered cross-sectional data containing 98,047 samples based on the 2017 China Migrants Dynamic Survey 2017 (CMDS) and China Municipal Statistical Yearbook after robustness tests and heterogeneity analysis using probit models.Results(1) digital financial inclusion can effectively alleviate overwork among migrant workers; (2) the impact of digital finance on overwork is more significant for the new generation, digitized industries, and self-employed migrant workers; it is also more significant for the South, East, and small and medium-sized cities than for the North, the Midwest, and large cities; (3) job quality and income are crucial factors in how digital financial inclusion affects overwork among migrant workers. Digital financial inclusion can improve the quality of employment for migrant workers and alleviate overwork. However, the income substitution effect partially reduces the inhibitory impact of digital financial inclusion on overwork.ConclusionContinuously promote the development of digital inclusive finance, improve laws and regulations, and protect the labor rights and interests of migrant workers. At the same time, vocational training and skills upgrading for rural migrant workers should be strengthened to improve the quality of their employment so that they can leave the secondary labor market and enter the primary labor market